New AI Framework Addresses Label Noise in Gaze Estimation for Improved Generalization

A novel AI framework called See-Through-Noise (SeeTN) has been introduced to enhance the generalization capabilities of gaze estimation models by specifically addressing the detrimental impact of label noise. The research, documented in arXiv preprint 2604.16562v1, identifies that existing generalizable gaze estimation methods, despite significant progress, frequently neglect how imprecise gaze annotations affect model performance. The proposed solution constructs a semantic embedding space using a prototype-based transformation to maintain a consistent topological structure between gaze features and their corresponding continuous labels. This approach then measures feature-label affinity consistency to differentiate between noisy and clean data samples. The work represents the first comprehensive investigation into how label noise negatively influences generalization within the field of gaze estimation. Gaze estimation is noted as critically important for various real-world applications. The announcement type for the paper is listed as cross.